Title: Sample Size and Power Calculation for Bayesian Testing with Bayes Factor
Version: 1.0.1
Description: The goal of 'BayesPower' is to provide tools for Bayesian sample size determination and power analysis across a range of common hypothesis testing scenarios using Bayes factors. The main function, BayesPower_BayesFactor(), launches an interactive 'shiny' application for performing these analyses. The application also provides command-line code for reproducibility. Details of the methods are described in the tutorial by Wong, Pawel, and Tendeiro (2025) <doi:10.31234/osf.io/pgdac_v1>.
BugReports: https://github.com/tkWong3004/BayesPower/issues
License: GPL (≥ 3)
Encoding: UTF-8
RoxygenNote: 7.3.3
Imports: rlang, shiny, gsl, Rcpp, ExtDist, ggplot2, patchwork, rmarkdown, glue, hypergeo, rootSolve, shinyWidgets
LinkingTo: Rcpp, BH
NeedsCompilation: yes
Packaged: 2025-10-26 08:54:28 UTC; u971096
Author: Tsz Keung Wong [aut, cre], Samuel Pawel [aut], Jorge Tendeiro [aut]
Maintainer: Tsz Keung Wong <t.k.wong3004@gmail.com>
Repository: CRAN
Date/Publication: 2025-10-29 20:10:17 UTC

Bayes factor for a Bayesian one-proportion test

Description

Calculate the Bayes factor (BF10) for a test of a single proportion, either against a point null or an interval null hypothesis.

Usage

BF10.bin.test(x, n, alpha, beta, location, scale, model, hypothesis, e = NULL)

Arguments

x

Observed number of successes.

n

Sample size.

alpha

Parameter for the analysis beta prior under the alternative hypothesis.

beta

Parameter for the analysis beta prior under the alternative hypothesis.

location

Null proportion value.

scale

Scale parameter for the analysis prior (if applicable, e.g., for Moment prior).

model

Statistical model of the analysis prior under the alternative hypothesis: beta prior ("beta") or Moment prior ("Moment").

hypothesis

The hypothesis being tested: two-sided ("!="), right-sided (">"), or left-sided ("<").

e

Optional numeric vector specifying bounds for an interval null; used if interval BF is calculated.

Value

The Bayes factor (BF10) for the one-proportion test.

Examples

BF10.bin.test(
  x = 12,
  n = 50,
  alpha = 2,
  beta = 3,
  location = 0.5,
  scale = 1,
  model = "beta",
  hypothesis = "!="
)

Bayes factor for a Bayesian correlation test

Description

Calculate the Bayes factor (BF10) for a correlation, either against a point null or an interval null hypothesis.

Usage

BF10.cor(
  r,
  n,
  k,
  alpha,
  beta,
  h0,
  hypothesis,
  location,
  scale,
  dff,
  model,
  e = NULL
)

Arguments

r

Observed correlation coefficient.

n

Sample size.

k

Parameter for the analysis default beta prior under the alternative hypothesis.

alpha

Parameter for the analysis beta prior under the alternative hypothesis.

beta

Parameter for the analysis beta prior under the alternative hypothesis.

h0

Null value of the correlation.

hypothesis

The hypothesis being tested: two-sided ("!="), right-sided (">"), or left-sided ("<").

location

Location parameter for the analysis prior under the alternative hypothesis.

scale

Scale parameter for the analysis normal moment prior under the alternative hypothesis.

dff

Degrees of freedom for the analysis prior under the alternative hypothesis (if applicable).

model

Statistical model of the analysis prior under the alternative hypothesis: default beta ("d_beta"), beta ("beta"), or normal moment ("NLP").

e

Optional numeric vector specifying bounds for an interval null; used if interval BF is calculated.

Value

The Bayes factor (BF10) for the correlation test.

Examples

BF10.cor(
  r = 0.3,
  n = 50,
  k = 1,
  alpha = 0.05,
  beta = 0.2,
  h0 = 0,
  hypothesis = "!=",
  location = 0,
  scale = 1,
  dff = 49,
  model = "d_beta"
)

Bayes factor for a Bayesian F-test

Description

Calculate the Bayes factor (BF10) for an F-test, either against a point null or an interval null hypothesis.

Usage

BF10.f.test(fval, df1, df2, dff, rscale, f_m, model, e = NULL)

Arguments

fval

Observed F-value from the F-test.

df1

Degrees of freedom for the numerator of the F-test.

df2

Degrees of freedom for the denominator of the F-test.

dff

Degrees of freedom for the analysis prior under the alternative hypothesis (if applicable).

rscale

Scaling parameter for the analysis effect size prior.

f_m

Cohen's f effect size parameter for the analysis prior.

model

Statistical model of the analysis prior under the alternative hypothesis: effect size prior ("effectsize") or Moment prior ("Moment").

e

Optional numeric vector specifying bounds for an interval null; used if interval BF is calculated.

Value

The Bayes factor (BF10) for the F-test.

Examples

BF10.f.test(
  fval = 4.5,
  df1 = 2,
  df2 = 12,
  dff = 12,
  rscale = 0.707,
  f_m = .1,
  model = "effectsize"
)

Bayes factor for a Bayesian test of two proportions

Description

Calculate the Bayes factor (BF10) for comparing two proportions using a Bayesian framework.

Usage

BF10.props(a0, b0, a1, b1, a2, b2, n1, n2, x1, x2)

Arguments

a0

Alpha parameter of the beta distribution under the null hypothesis.

b0

Beta parameter of the beta distribution under the null hypothesis.

a1

Alpha parameter of the analysis beta prior distribution for group 1 under the alternative hypothesis.

b1

Beta parameter of the analysis beta prior distribution for group 1 under the alternative hypothesis.

a2

Alpha parameter of the analysis beta prior distribution for group 2 under the alternative hypothesis.

b2

Beta parameter of the analysis beta prior distribution for group 2 under the alternative hypothesis.

n1

Sample size for group 1.

n2

Sample size for group 2.

x1

Observed number of successes for group 1.

x2

Observed number of successes for group 2.

Value

The Bayes factor (BF10) for comparing two proportions.

Examples

BF10.props(
  a0 = 2, b0 = 3,
  a1 = 2, b1 = 3,
  a2 = 2, b2 = 3,
  n1 = 50, n2 = 60,
  x1 = 25, x2 = 30
)

Bayes factor for one-sample Bayesian t-test

Description

Calculate the Bayes factor (BF10) for a one-sample Bayesian t-test, either against a point null or an interval null hypothesis.

Usage

BF10.t.test.one_sample(
  tval,
  df,
  model,
  location,
  scale,
  dff,
  hypothesis,
  e = NULL
)

Arguments

tval

Observed t-value from the one-sample t-test.

df

Degrees of freedom for the t-test.

model

Statistical model of the analysis prior under the alternative hypothesis: Normal distribution ("Normal"), Normal moment ("NLP"), or scaled t ("t-distribution").

location

Location parameter for the analysis prior under the alternative hypothesis.

scale

Scale parameter for the analysis prior under the alternative hypothesis.

dff

Degrees of freedom for the analysis prior under the alternative hypothesis (if applicable).

hypothesis

The hypothesis being tested: two-sided ("!="), right-sided (">"), or left-sided ("<").

e

Optional numeric vector specifying bounds for an interval null; used if interval BF is calculated.

Value

The Bayes factor (BF10) for the one-sample t-test.

Examples

BF10.t.test.one_sample(
  tval = 2.31,
  df = 29,
  model = "t-distribution",
  location = 0,
  scale = 0.707,
  dff = 1,
  hypothesis = "!="
)

Bayes factor for two-sample Bayesian t-test

Description

Calculate the Bayes factor (BF10) for a two-sample Bayesian t-test, either against a point null or an interval null hypothesis.

Usage

BF10.t.test.two_sample(
  tval,
  N1,
  N2,
  model,
  location,
  scale,
  dff,
  hypothesis,
  e = NULL
)

Arguments

tval

Observed t-value from the two-sample t-test.

N1

Sample size of group 1.

N2

Sample size of group 2.

model

Statistical model of the analysis prior under the alternative hypothesis: Normal distribution ("Normal"), Normal moment ("NLP"), or scaled t ("t-distribution").

location

Location parameter for the analysis prior under the alternative hypothesis.

scale

Scale parameter for the analysis prior under the alternative hypothesis.

dff

Degrees of freedom for the analysis prior under the alternative hypothesis (if applicable).

hypothesis

The hypothesis being tested: two-sided ("!="), right-sided (">"), or left-sided ("<").

e

Optional numeric vector specifying bounds for an interval null; used if interval BF is calculated.

Value

The Bayes factor (BF10) for the two-sample t-test.

Examples

BF10.t.test.two_sample(
  tval = 2.1,
  N1 = 30,
  N2 = 30,
  model = "t-distribution",
  location = 0,
  scale = 0.707,
  dff = 1,
  hypothesis = "!="
)

Sample size determination for Bayesian one-proportion test

Description

Perform sample size determination or the calculation of compelling and misleading evidence for a Bayesian test of a single proportion.

Usage

BFpower.bin(
  hypothesis = NULL,
  interval = NULL,
  D = NULL,
  target = NULL,
  FP = NULL,
  location = NULL,
  model = NULL,
  alpha = NULL,
  beta = NULL,
  scale = NULL,
  model_d = NULL,
  alpha_d = NULL,
  beta_d = NULL,
  location_d = NULL,
  scale_d = NULL,
  de_an_prior = NULL,
  N = NULL,
  mode_bf = NULL,
  e = NULL,
  direct = NULL,
  h0 = NULL
)

Arguments

hypothesis

The hypothesis being tested (e.g., two-sided "!=", right-sided ">", left-sided "<").

interval

Character or integer (0 or 1). If "1", Bayes factor with a point null against a composite alternative hypothesis; otherwise Bayes factor with interval null and alternative hypotheses.

D

The bound of compelling evidence.

target

The targeted true positive rate (if direct = "h1") or true negative rate (if direct = "h0").

FP

The targeted false positive rate (if direct = "h1") or false negative rate (if direct = "h0").

location

Null proportion value.

model

Statistical model of the analysis prior under the alternative hypothesis: beta prior ("beta") or Moment prior ("Moment")

alpha

Parameter for the analysis prior under the alternative hypothesis.

beta

Parameter for the analysis prior under the alternative hypothesis.

scale

Scale parameter for the analysis prior under the alternative hypothesis.

model_d

Statistical model of the design prior under the alternative hypothesis:beta prior ("beta") , Moment prior ("Moment"), or Point prior ("Point")

alpha_d

Parameter for the design prior under the alternative hypothesis.

beta_d

Parameter for the design prior under the alternative hypothesis.

location_d

The proportion value for the design point prior.

scale_d

Scale parameter for the design prior under the alternative hypothesis.

de_an_prior

Integer (0 or 1). If 1, analysis and design priors under the alternative are the same; if 0, they are not.

N

Sample size.

mode_bf

Integer (0 or 1). If 1, sample size determination; if 2, N is needed for the calculation of probabilities of compelling and misleading evidence.

e

The bounds for the interval Bayes factor (used when interval = 0).

direct

If "h1", BF10; if "h0", BF01.

h0

Null value

Value

A data frame with the following columns:

If sample size determination fails, the function returns NULL.

Examples

BFpower.bin(
  hypothesis = "!=",
  interval = "1",
  D = 3,
  target = 0.8,
  FP = 0.05,
  location = 0.5,
  model = "beta",
  alpha = 1,
  beta = 1,
  de_an_prior = 1,
  mode_bf = 1,
  direct = "h1"
)

Sample size determination for Bayesian correlation test

Description

Perform sample size determination or the calculation of compelling and misleading evidence for a Bayesian correlation test.

Usage

BFpower.cor(
  hypothesis = NULL,
  h0 = NULL,
  e = NULL,
  interval = NULL,
  D = NULL,
  target = NULL,
  FP = NULL,
  model = NULL,
  k = NULL,
  alpha = NULL,
  beta = NULL,
  scale = NULL,
  model_d = NULL,
  alpha_d = NULL,
  beta_d = NULL,
  location_d = NULL,
  k_d = NULL,
  scale_d = NULL,
  de_an_prior = NULL,
  N = NULL,
  mode_bf = NULL,
  direct = NULL
)

Arguments

hypothesis

The hypothesis being tested (e.g., two-sided "!=", right-sided ">", left-sided "<").

h0

Null value of the correlation.

e

The bounds for the interval Bayes factor (used when interval = 0).

interval

Character or integer (0 or 1). If "1", Bayes factor with a point null against a composite alternative hypothesis; otherwise Bayes factor with interval null and alternative hypotheses.

D

The bound of compelling evidence.

target

The targeted true positive rate (if direct = "h1") or true negative rate (if direct = "h0").

FP

The targeted false positive rate (if direct = "h1") or false negative rate (if direct = "h0").

model

Statistical model of the analysis prior under the alternative hypothesis: default beta ("d_beta"), beta ("beta"), or normal moment ("NLP")

k

Parameter for the analysis default beta prior under the alternative hypothesis.

alpha

Parameter for the analysis beta prior under the alternative hypothesis.

beta

Parameter for the analysis beta prior under the alternative hypothesis.

scale

Scale parameter for the analysis normal moment prior under the alternative hypothesis.

model_d

Statistical model of the design prior under the alternative hypothesis:default beta ("d_beta"), beta ("beta"), normal moment ("NLP" , or point "Point")

alpha_d

Parameter for the design beta prior under the alternative hypothesis.

beta_d

Parameter for the design beta prior under the alternative hypothesis.

location_d

Location parameter for the design point prior under the alternative hypothesis.

k_d

Parameter for the design default beta prior under the alternative hypothesis.

scale_d

Scale parameter for the design normal moment prior under the alternative hypothesis.

de_an_prior

Integer (0 or 1). If 1, analysis and design priors under the alternative are the same; if 0, they are not.

N

Sample size.

mode_bf

Integer (0 or 1). If 1, sample size determination; if 2, N is needed for the calculation of probabilities of compelling and misleading evidence.

direct

If "h1", BF10; if "h0", BF01.

Value

A data frame with the following columns:

If sample size determination fails, the function returns NULL.

Examples

BFpower.cor(
  hypothesis = "!=",
  h0 = 0,
  e = NULL,
  interval = "1",
  D = 3,
  target = 0.8,
  FP = 0.05,
  model = "d_beta",
  k = 1,
  de_an_prior = 1,
  mode_bf = 1,
  direct = "h1"
)

Sample size determination for Bayesian F-test

Description

Perform sample size determination or the calculation of compelling and misleading evidence for a Bayesian F-test.

Usage

BFpower.f(
  interval = NULL,
  D = NULL,
  target = NULL,
  FP = NULL,
  p = NULL,
  k = NULL,
  model = NULL,
  dff = NULL,
  rscale = NULL,
  f_m = NULL,
  model_d = NULL,
  dff_d = NULL,
  rscale_d = NULL,
  f_m_d = NULL,
  de_an_prior = NULL,
  N = NULL,
  mode_bf = NULL,
  direct = NULL,
  e = NULL
)

Arguments

interval

Character or integer (0 or 1). If "1", Bayes factor with a point null against a composite alternative hypothesis; otherwise Bayes factor with interval null and alternative hypotheses.

D

The bound of compelling evidence.

target

The targeted true positive rate (if direct = "h1") or true negative rate (if direct = "h0").

FP

The targeted false positive rate (if direct = "h1") or false negative rate (if direct = "h0").

p

Number of predictors in the reduced model.

k

Number of predictors in the full model.

model

Statistical model of the analysis prior under the alternative hypothesis: effect size prior ("effectsize") or Moment prior ("Moment")

dff

Degrees of freedom for the analysis prior under the alternative hypothesis.(must be >3 if moment prior is used)

rscale

Scaling parameter for the analysis effect size prior.

f_m

Cohen's f effect size parameter for the analysis prior.

model_d

Statistical model of the design prior under the alternative hypothesis:: effect size prior ("effectsize"), Moment prior ("Moment"), or Point prior ("Point")

dff_d

Degrees of freedom for the design prior under the alternative hypothesis. (must be >3 if moment prior is used)

rscale_d

Scaling parameter for the design effect size prior.

f_m_d

Cohen's f effect size parameter for the design prior or the point design prior.

de_an_prior

Integer (0 or 1). If 1, analysis and design priors under the alternative are the same; if 0, they are not.

N

Sample size.

mode_bf

Integer (0 or 1). If 1, sample size determination; if 2, N is needed for the calculation of probabilities of compelling and misleading evidence.

direct

If "h1", BF10; if "h0", BF01.

e

The bounds for the interval Bayes factor (used when interval = 0).

Value

A data frame with the following columns:

If sample size determination fails, the function returns NULL.

Examples

BFpower.f(
 inter = "1",
 D = 3,
 target = 0.8,
 p = 1,
 k = 2,
 model = "Moment",
 dff = 1,
 f_m = 0.1,
 de_an_prior = 1,
 mode_bf = 1,
 direct = "h1"
)

Sample size determination for Bayesian test of two proportions

Description

Perform sample size determination or the calculation of compelling and misleading evidence for a Bayesian comparison of two proportions.

Usage

BFpower.props(
  D = NULL,
  target = NULL,
  a0 = NULL,
  b0 = NULL,
  a1 = NULL,
  b1 = NULL,
  a2 = NULL,
  b2 = NULL,
  model1 = NULL,
  a1d = NULL,
  b1d = NULL,
  dp1 = NULL,
  model2 = NULL,
  a2d = NULL,
  b2d = NULL,
  dp2 = NULL,
  mode_bf = NULL,
  n1 = NULL,
  n2 = NULL,
  direct = NULL
)

Arguments

D

The bound of compelling evidence.

target

The targeted true positive rate (if direct = "h1") or true negative rate (if direct = "h0").

a0

Alpha parameter of the beta distribution under the null .

b0

Beta parameter of the beta distribution under the null.

a1

Alpha parameter of the analysis beta prior distribution for group 1 under the alternative hypothesis.

b1

Beta parameter of the analysis beta prior distribution for group 1 under the alternative hypothesis.

a2

Alpha parameter of the analysis beta prior distribution for group 2 under the alternative hypothesis.

b2

Beta parameter of the analysis beta prior distribution for group 2 under the alternative hypothesis.

model1

Statistical model of the design prior for group 1: beta ("beta"), Point prior ("Point", or same as analysis prior "same")

a1d

Alpha parameter for the design prior of group 1.

b1d

Beta parameter for the design prior of group 1.

dp1

True proportion for group 1 in the design prior.

model2

Statistical model of the design prior for group 1: beta ("beta"), or Point prior ("Point", or same as analysis prior "same")

a2d

Alpha parameter for the design prior of group 2.

b2d

Beta parameter for the design prior of group 2.

dp2

True proportion for group 2 in the design prior.

mode_bf

Integer (0 or 1). If 1, sample size determination; if 2, n1 and n2 are used for the calculation of probabilities of compelling and misleading evidence.

n1

Sample size for group 1.

n2

Sample size for group 2.

direct

If "h1", BF10; if "h0", BF01.

Value

A data frame with the following columns:

If sample size determination fails, the function returns NULL.

Examples

BFpower.props(
  D = 3,
  target = 0.8,
  a0 = 1,
  b0 = 1,
  model1 = "same",
  a1 = 1,
  b1 = 1,
  a2 = 1,
  b2 = 1,
  model2 = "same",
  mode_bf = 1,
  direct = "h1"
)

Sample size determination for one-sample Bayesian t-test

Description

Perform sample size determination or the calculation of compelling and misleading evidence.

Usage

BFpower.t.test_one_sample(
  hypothesis = NULL,
  e = NULL,
  interval = NULL,
  D = NULL,
  target = NULL,
  alpha = NULL,
  model = NULL,
  location = NULL,
  scale = NULL,
  dff = NULL,
  model_d = NULL,
  location_d = NULL,
  scale_d = NULL,
  dff_d = NULL,
  de_an_prior = NULL,
  N = NULL,
  mode_bf = NULL,
  direct = NULL
)

Arguments

hypothesis

The hypothesis being tested (e.g., two-sided "!=", right-sided ">", left-sided "<").

e

The bounds for the interval Bayes factor (used when interval = 0).

interval

Integer (1 or 0). If 1, Bayes factor with a point null against a composite alternative hypothesis; otherwise Bayes factor with interval null and alternative hypotheses.

D

The bound of compelling evidence.

target

The targeted true positive rate (if direct = "h1") or true negative rate (if direct = "h0").

alpha

The targeted false positive rate (if direct = "h1") or false negative rate (if direct = "h0").

model

Statistical model of the analysis prior under the alternative hypothesis: Normal distribution ("Normal"), Normal moment ("NLP"), or scaled t ("t-distribution").

location

Location parameter for the analysis prior under the alternative hypothesis.

scale

Scale parameter for the analysis prior under the alternative hypothesis.

dff

Degrees of freedom for the analysis prior under the alternative hypothesis (if applicable).

model_d

Statistical model of the design prior under the alternative hypothesis: Normal distribution ("Normal"), Normal moment ("NLP"), or scaled t ("t-distribution").

location_d

Location parameter for the design prior under the alternative hypothesis.

scale_d

Scale parameter for the design prior under the alternative hypothesis.

dff_d

Degrees of freedom parameter for the design prior under the alternative hypothesis.

de_an_prior

Integer (0 or 1). If 1, analysis and design priors under the alternative are the same; if 0, they are not.

N

Sample size.

mode_bf

Integer (1 or 2). If 1, sample size determination; if 2, N is used for the calculation of probabilities of compelling and misleading evidence.

direct

If "h1", controlling true/false positive rates; if "h0", controlling true/false negative rates.

Value

A data frame with the following columns:

If sample size determination fails, the function returns NULL.

Examples

BFpower.t.test_one_sample(
  hypothesis = "!=",
  interval = 1,
  D = 3,
  target = 0.8,
  alpha = 0.05,
  model = "t-distribution",
  location = 0,
  scale = 0.707,
  dff = 1,
  de_an_prior = 1,
  N = NULL,
  mode_bf = 1,
  direct = "h1"
)

Sample size determination for two-sample Bayesian t-test

Description

Perform sample size determination or the calculation of compelling and misleading evidence for a two-sample Bayesian t-test.

Usage

BFpower.t.test_two_sample(
  hypothesis = NULL,
  e = NULL,
  interval = NULL,
  D = NULL,
  target = NULL,
  alpha = NULL,
  model = NULL,
  location = NULL,
  scale = NULL,
  dff = NULL,
  model_d = NULL,
  location_d = NULL,
  scale_d = NULL,
  dff_d = NULL,
  de_an_prior = NULL,
  N1 = NULL,
  N2 = NULL,
  r = NULL,
  mode_bf = NULL,
  direct = NULL
)

Arguments

hypothesis

The hypothesis being tested (e.g., two-sided "!=", right-sided ">", left-sided "<").

e

The bounds for the interval Bayes factor (used when interval = 0).

interval

Integer (1 or 0). If 1, Bayes factor with a point null against a composite alternative hypothesis; otherwise Bayes factor with interval null and alternative hypotheses.

D

The bound of compelling evidence.

target

The targeted true positive rate (if direct = "h1") or true negative rate (if direct = "h0").

alpha

The targeted false positive rate (if direct = "h1") or false negative rate (if direct = "h0").

model

Statistical model of the analysis prior under the alternative hypothesis: Normal distribution ("Normal"), Normal moment ("NLP"), or scaled t ("t-distribution").

location

Location parameter for the analysis prior under the alternative hypothesis.

scale

Scale parameter for the analysis prior under the alternative hypothesis.

dff

Degrees of freedom for the analysis prior under the alternative hypothesis (if applicable).

model_d

Statistical model of the design prior under the alternative hypothesis: Normal distribution ("Normal"), Normal moment ("NLP"), or scaled t ("t-distribution").

location_d

Location parameter for the design prior under the alternative hypothesis.

scale_d

Scale parameter for the design prior under the alternative hypothesis.

dff_d

Degrees of freedom parameter for the design prior under the alternative hypothesis.

de_an_prior

Integer (0 or 1). If 1, analysis and design priors under the alternative are the same; if 0, they are not.

N1

Sample size of group 1.

N2

Sample size of group 2.

r

Ratio of the sample size of group 2 over group 1 (N2 / N1).

mode_bf

Integer (1 or 0). If 1, sample size determination; if 0, N1 and N2 are used for the calculation of probabilities of compelling and misleading evidence.

direct

If "h1", controls true/false positive rates (BF10); if "h0", controls true/false negative rates (BF01).

Value

A data frame with the following columns:

If sample size determination fails, the function returns NULL.

Examples

BFpower.t.test_two_sample(
  hypothesis = "!=",
  e = NULL,
  interval = 1,
  D = 3,
  target = 0.8,
  alpha = 0.05,
  model = "t-distribution",
  location = 0,
  scale = 0.707,
  dff = 1,
  de_an_prior = 1,
  r = 1,
  mode_bf = 1,
  direct = "h1"
)

Launch the BayesPower Shiny Application

Description

This function starts the interactive Shiny application for Bayesian power analysis using Bayes factors. The app provides a graphical user interface built with shiny.

Usage

BayesPower_BayesFactor()

Details

The application includes both the UI and server components, which are defined internally in the package. When run, a browser window or RStudio viewer pane will open to display the interface.

Value

No return value, called for its side effects.

Examples

if (interactive()) {
  # Launch the Shiny application
  BayesPower_BayesFactor()
}